As proposed in the original publication [1]}, the model exists in two variants. One variant of this model uses strided convolutions for training, as it is takes better care of unused intermediate results in the hidden layers. The other variant uses dilated convolutions which uses all the intermediate results for the prediction of the 3D pose output. In our case, we carried out experiments using strided convolutions both for training and prediction of 3D pose coordinates.
Furthermore, we used symmetric convolutions only in the model, as causal convolutions are rather practical for real-time inference scenarios[1]}.
